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r - 使用 R 对数据框中成对变量进行格兰杰因果关系分析

转载 作者:行者123 更新时间:2023-12-04 08:18:21 26 4
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我想在国家年数据的几个不同变量对之间进行格兰杰因果关系。我似乎能够让它在循环/函数之外工作,但是在将它与我的其余代码集成时遇到了一些麻烦。我在下面提供了一个最小工作示例和所需输出的代码。任何帮助将不胜感激。先感谢您!
编辑 : 我应该在原帖中更清楚地说明。此生成数据中的每一列都包含多个国家/地区的时间序列数据。我想对各个国家/地区进行平均,然后使用下面详述的相同方法对这些变量执行 granger 操作
模拟时间序列数据的代码

library(tidyindexR)
library(tidyverse)
library(dplyr)
library(reshape2)
library(lmtest)

simulateCountryData = function(N=90, NEACH = 30, SEED=100){

# Set seed for reproducibility
set.seed(SEED)

#"""
# This function simulates
# N is the total number of observations you want across all subjecs/geocodes
# NEACH is the number of observations per geocode/sample
# see https://blog.revolutionanalytics.com/2009/02/how-to-choose-a-random-number-in-r.html for more reources
#"""

variableOne<-rnorm(N,sample(1:100, NEACH),0.5)
variableOne[variableOne<0]<-0

variableTwo<-rnorm(N,sample(100:1, NEACH),0.5)
variableTwo[variableTwo<0]<-0

variableThree<-rnorm(N,sample(1:100, NEACH),0.5)
variableThree[variableTwo<0]<-0

variableFour<-rnorm(N,sample(1:100, NEACH),0.5)
variableFour[variableFour<0]<-0

variableFive<-rnorm(N,sample(1:100, NEACH),0.5)
variableFive[variableFive<0]<-0

# Defining the subjects/geocodes
# Makes a sequence of 1:N/NEACH such that the geocode appears NEACH times (i.e. 1,60/3,each=3)
geocodeNum<-factor(rep(seq(1,N/NEACH),each=NEACH))

# Defining the years/time periods to be repeated
# Lists the tear 3x each (3 years per subject/geocode)
year<-rep(seq(2000,2000+NEACH-1,1),N/NEACH)

# Putting it all together
AllData<-data.frame(geocodeNum,
year,
variableOne,
variableTwo,
variableThree,
variableFour,
variableFive)

return(AllData)
}

mySimData = simulateCountryData()
在数据帧中提取皮尔逊相关性和格式的代码
信用转至 the-mad-statter谁好心提供了答案 here
# matrix of unique pairs coded as numeric
mx_combos <- combn(1:length(myVariables), 2)
# list of unique pairs coded as numeric
ls_combos <- split(mx_combos, rep(1:ncol(mx_combos), each = nrow(mx_combos)))
# for each pair in the list, create a 1 x 4 dataframe
ls_rows <- lapply(ls_combos, function(p) {
# lookup names of variables
v1 <- myVariables[p[1]]
v2 <- myVariables[p[2]]
# perform the cor.test()
htest <- cor.test(mySimData[[v1]], mySimData[[v2]])
# record pertinent info in a dataframe
data.frame(Var1 = v1,
Var2 = v2,
Pval = htest$p.value,
Rval = unname(htest$estimate))
})
# row bind the list of dataframes

my_output = dplyr::bind_rows(ls_rows)
此函数生成以下数据帧:
        Var1          Var2        Pval        Rval
variableFive variableFour 0.749283286 -0.03415477
variableFive variableOne 0.865119584 -0.01815724
variableFive variableThree 0.004120881 -0.29960240
variableFive variableTwo 0.024713897 0.23666723
variableFour variableOne 0.249864859 0.12254729
variableFour variableThree 0.587474758 0.05794634
variableFour variableTwo 0.624329543 0.05231733
variableOne variableThree 0.056216554 -0.20200708
variableOne variableTwo 0.368598424 -0.09589547
变量三变量二 0.056192121 -0.20202672
我想在 my_output 中的每个成对变量之间添加格兰杰因果关系分析的 p 值。数据框作为附加列。目前,我可以为特定的成对比较提取 pvalues,但我无法弄清楚如何以简单的方式对所有成对变量执行此操作(因为在实际示例中还有更多变量)。如果有人能指出我正确的方向或提供解决方案,那就太棒了。
格兰杰分析代码
此代码似乎对各国每年的数据进行平均,然后在变量之间执行格兰杰因果关系。我只需要一些帮助来弄清楚如何改变它以使用 my_output数据框。
a= mySimData %>% 
select(geocodeNum, year,variableFive) %>%
group_by(year) %>%
summarize(mean(variableFive))

b = mySimData %>%
select(geocodeNum, year,variableTwo) %>%
group_by(year) %>%
summarize(mean(variableTwo))

c = left_join(a,b)

d = grangertest(c$`mean(variableFive)` ~ c$`mean(variableTwo)`)
d[2,4]
所需输出
理想情况下,输出应如下所示(尽管格兰杰的 P 值正确):
        Var1          Var2        Pval        Rval Granger
variableFive variableFour 0.749283286 -0.03415477 0.050
variableFive variableOne 0.865119584 -0.01815724 0.021
variableFive variableThree 0.004120881 -0.29960240 0.090
variableFive variableTwo 0.024713897 0.23666723 0.042
variableFour variableOne 0.249864859 0.12254729 0.050
variableFour variableThree 0.587474758 0.05794634 0.021
variableFour variableTwo 0.624329543 0.05231733 0.092
variableOne variableThree 0.056216554 -0.20200708 0.046
variableOne variableTwo 0.368598424 -0.09589547 0.072
variableThree variableTwo 0.056192121 -0.20202672 0.033
再次感谢阅读。

最佳答案

您可以将该函数应用于 combn 中的所有组合。函数本身,所以不需要单独的 lapply称呼。

cols <- grep('variable', names(mySimData), value = TRUE)

result <- do.call(rbind, combn(cols, 2, function(p) {
v1 <- p[1]
v2 <- p[2]
# perform the cor.test()
htest <- cor.test(mySimData[[v1]], mySimData[[v2]])
val1 <- aggregate(mySimData[[v1]], list(mySimData$year), mean)[[2]]
val2 <- aggregate(mySimData[[v2]], list(mySimData$year), mean)[[2]]
#perform grangertest
mod <- lmtest::grangertest(val1, val2)
# record pertinent info in a dataframe
data.frame(Var1 = v1,
Var2 = v2,
Pval = htest$p.value,
Rval = unname(htest$estimate),
Granger = mod[2, 4])
}, simplify = FALSE))

result

# Var1 Var2 Pval Rval Granger
#1 variableOne variableTwo 0.368598 -0.09590 0.8309
#2 variableOne variableThree 0.056217 -0.20201 0.2831
#3 variableOne variableFour 0.249865 0.12255 0.8860
#4 variableOne variableFive 0.865120 -0.01816 0.8842
#5 variableTwo variableThree 0.056192 -0.20203 0.9049
#6 variableTwo variableFour 0.624330 0.05232 0.7993
#7 variableTwo variableFive 0.024714 0.23667 0.7060
#8 variableThree variableFour 0.587475 0.05795 0.2510
#9 variableThree variableFive 0.004121 -0.29960 0.2813
#10 variableFour variableFive 0.749283 -0.03415 0.8396

关于r - 使用 R 对数据框中成对变量进行格兰杰因果关系分析,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/65607201/

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